Our main goal was to improve the overall visual performance of SR. In this section, we describe our improved network architecture. The main difference between the GAN and WGAN[1]} is that the sigmoid function and batch normalization (BN)[2]} layer of the discriminator network are removed. The entire neural network is stabilized by gradient punishment[1]}. It has been shown that removing the BN layer improves performance and reduces complexity[4]}, [5]}. Further, the removal of the BN layer contributes to improving the robustness of the network and reduces the computational complexity and memory consumption. We replaced the original basic block with a heterogeneous kernel-based residual block (HetResidual block), which includes HetConv, as depicted in Section REF . The HetResidual block is the basic network building unit. This block requires fewer parameters than the original basic block, improves network performance, and reduces computational complexity. More parameters may lead to a higher probability of mode collapse[4]}, [7]}, so reducing the total number of parameters is beneficial.
For the discriminator network, we deepened the network structure and experimentally demonstrated that this modification improves image quality. The detailed experimental results are given in Section . According to the characteristics of the heterogeneous kernel-based convolutions, we used a skip connection when designing the generator network structure.
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